A significant and important problem in digital image processing is automatic detection of people in digital images. A further problem is the determination and classification of the age of people in digital images. Reliable automatic age classification of human faces in a digital image would enable many applications.
U.S. Pat. No. 5,781,650, to Lobo et al. describes a method of classifying the age of a face in a digital image using ratios of certain distances derived from the face and using snakelets to perform a wrinkle analysis. This approach has some shortcomings. It can be difficult to locate facial features in an image with a high degree of confidence. Wrinkles appear at vastly different ages for different people. The presence of wrinkles is highly dependent on facial expression. Adding more ambiguity to the problem is that many aging people who begin to develop wrinkles opt for plastic surgery and other medical procedures to remove wrinkles.
Redeye detection is well known. Examples of techniques for this purpose are disclosed in U.S. Pat. No. 6,292,574 to Schildkraut et al. and PCT Patent publication WO 9917254.
Generally, when an eye exhibits the redeye defect in an image, the pupil is at least partially dilated. The maximum pupil dilation is a function of age. With aging, there is deterioration in vision in low light conditions. There are neural losses, but the major decline is due to changes in the eye's optics. The lens and other optical media become more opaque and the lens becomes yellower, allowing less light entering the eye to reach the photoreceptors and making discrimination of blue colors more difficult. The relative size of the pupil shrinks, allowing less light to enter the eye. The pupil's response to dim light decreases with age and becomes virtually nil by age 80. Table 1 shows how the pupil size shrinks with age.
TABLE 1Pupil Size as a function of AgeDay -Night -Agepupil sizepupil size(yr)(in mm)(in mm)204.78.0304.37.0403.96.0503.55.0603.14.1702.73.2802.32.5(Downloaded on the date of Aug. 31, 2004, from the Internet address: http://www.visualexpert.com/Resources/olderdrivers.html.)
A method by which the head pose (out-of-plane rotation relative to a subject plane of a digital image) can be derived through automatic analysis is disclosed in “Estimating Facial Pose Using the EM Algorithm”, K. Choi et al., Ninth British Machine Vision Conference, 1998, downloaded on the date of Aug. 31, 2004, from the Internet at: http://www.bmva.ac.uk/bmvc/1998/pdf/p 147.pdf.
It would thus be desirable to provide improvements in classifying the age of a human by analyzing an image of the human's face.